The present invention relates generally to analytical techniques for diagnosing machine conditions, and more particularly to the use of advanced stress wave analysis techniques to detect discrepant conditions in operating machinery.
Stress Wave Analysis (SWAN) is an ultrasonic technique for the real time, in-situ, measurement of friction and shock. The use of “stress waves” and their analysis is the topic of a number of patents, which will be briefly described hereinbelow:
U.S. Pat. No. 4,530,240, titled “Method and Apparatus for Diagnosing Machine Condition, and which is incorporated herein by reference, teaches a means for predicting machine failure by monitoring stress waves produced by friction and shock events.
U.S. Pat. No. 5,852,793, titled: METHOD AND APPARATUS FOR PREDICTIVE DIAGNOSIS OF MOVING MACHINE PARTS, and incorporated herein by reference, describes Stress Wave Analysis (SWAN) technology resulting from more than a decade of research and development activity. The technology includes analog and digital hardware designs, as well as software, that significantly increase signal to noise ratio, implement SWAN technology in low cost PC based platforms, and provide data logging and predictive maintenance capability. The disclosed method includes new ways of displaying SWAN data for simplified analysis, as well as Time Domain Feature Extraction software that provides “intelligent data compression” for use with Artificial Intelligence software.
U.S. Pat. No. 6,351,713, titled: DISTRIBUTED STRESS WAVE ANALYSIS SYSTEM, and incorporated herein by reference, discloses a next generation of SWAN products, which combine Stress Wave Analysis with Artificial Intelligence to provide automation to the interpretation of SWAN data. This improvement provides a further reduction in the skill levels and training required to use SWAN technology for accurate predictive maintenance, and extends SWAN capabilities for fault location/isolation and remaining useful life projection. A Frequency Domain Feature Extraction method and a proprietary Data Fusion Architecture are disclosed for providing very accurate fault detection, with very low probability of false alarms. The hardware designs described in this patent provide additional improvement of signal to noise ratio, while significantly reducing the size, weight, and power consumption of SWAN hardware, so that it becomes more practical for a variety of mobile and fixed base applications.
U.S. Pat. No. 6,499,350 titled: FOREIGN OBJECT DETECTION (FOD), and incorporated herein by reference, teaches the use of a specialized hardware implementation of SWAN technology for application to turbo machinery, which can be seriously damaged by the ingestion of foreign objects. The disclosed design is applicable for airborne, marine, and ground based applications.
U.S. Pat. No. 6,684,700 titled: STRESS WAVE SENSOR, incorporated herein by reference, defines functional performance requirements for a sensor specifically designed to detect stress waves. This reference also defines the quantitative relationships between the sensor specifications and the analog signal conditioning that is used to filter, amplify, and demodulate the sensor's broad band output.
U.S. Pat. No. 6,553,839 titled: METHOD FOR STIMULATING A SENSOR AND MEASURING THE SENSOR'S OUTPUT OVER A FREQUENCY RANGE and incorporated by reference, describes a calibration technique tailored to the peculiar functional specifications of certain stress wave sensors.
U.S. Pat. No. 6,679,119 titled: MULTI-FUNCTION SENSOR, and incorporated herein by reference, teaches that, for many predictive maintenance applications, SWAN and vibration analysis are complimentary technologies. The sensor described in this patent provides electrical signals proportional to both vibration and stress waves from a single device. This multi-function sensor significantly reduces cost, weight and power requirements compared to separate sensors. This device is applicable for both airborne and industrial applications.
SWAN techniques, some of which are discussed on some of the above cited references, typically employ a specialized, externally mounted ultrasonic sensor along with unique signal conditioning to produce a Stress Wave Pulse Train (SWPT) time waveform. This SWPT is then digitized and analyzed to determine the “health” of the operating machine. Because friction is also a function of operational parameters, such as load and speed of the monitored machine, the analysis process should take these normal variables into account, to prevent false or premature indication of a discrepant condition. This has been accomplished by analyzing short (1-10 second) “snapshots” of data, taken at reference operating conditions.
However, the prior art techniques have suffered from a number of drawbacks, including a requirement that similar operating conditions be imposed to data “snapshots” in order to have an “apples-to-apples” comparison situation. Such a requirement is often impractical because it may be difficult, or impossible, to impose such uniformity in operating conditions. A way around such limitations in the SWAN process would be useful.